Listwise approaches based on feature ranking discovery

Abstract

Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these approaches was hindered. To address the limitations of previous listwise work, we propose a quasi-KNN model to discover the ranking of features and employ rank addition rule to calculate the weight of combination. On the basis of this, we propose three listwise algorithms, FeatureRank, BLFeatureRank, and DiffRank. The experimental results show that our proposed algorithms can be applied to a strict ordered ranking training set and gain better performance than state-of-the-art listwise algorithms.

Keywords

learning to rank / listwise approach / feature’s ranking discovery

References

[1] Crammer K, Singer Y. Pranking with ranking. In: Proceedings of the 2001 Neural Information Processing Systems. 2001, 641-647

[2] Li P, Burges C J C, Wu Q. Mcrank: learning to rank using multiple classification and gradient boosting. In: Proceedings of the 21st Annual Conference on Neural Information Processing Systems. 2007

[3] Cao Y, Xu J, Liu T Y, Li H, Huang Y, Hon H W. Adapting ranking SVM to document retrieval. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2006, 186-193

[4] Tsai M F, Liu T Y, Qin T, Chen H H, Ma W Y. FRank: a ranking method with fidelity loss. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2007, 383-390

[5] Freund Y, Iyer R D, Schapire R E, Singer Y. An efficient boosting algorithm for combining preferences. The Journal of Machine Learning Research, 2003, 4: 933-969

[6] Cao Z, Qin T, Liu T Y, Tsai M F, Li H. Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th International Conference on Machine Learning. 2007, 129-136

[7] Xia F, Liu T Y, Wang J, Zhang W, Li H. Listwise approach to learning to rank: theory and algorithm. In: Proceedings of the 25th International Conference on Machine Learning. 2008, 1192-1199

[8] Xu J, Li H. Adarank: a boosting algorithm for information retrieval. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2007, 391-398

[9] Yue Y, Finley T, Radlinski F, Joachims T. A support vector method for optimizing average precision. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2007, 271-278

[10] Qin T, Zhang X D, Tsai M F, Wang D S, Liu T Y, Li H. Query-level loss functions for information retrieval. Information Processing & Management, 2008, 44(2): 838-855

[11] Robertson S E. Overview of the okapi projects. Journal of Documentation, 1997, 53(1): 3-7

[12] Zhai C, Lafferty J D. A study of smoothing methods for language models applied to ad hoc information retrieval. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2001, 334-342

[13] Freund Y, Schapire R E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 1997, 55(1): 119-139

[14] Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting. The Annals of Statistics, 2000, 28(2): 337-373

[15] Schapire R E, Singer Y. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 1999, 37(3): 297-336

[16] Zheng Z, Zha H, Zhang T, Chapelle O, Chen K, Sun G.A general boosting method and its application to learning ranking functions for web search. In: Proceedings of the 21st Annual Conference on Neural Information Processing Systems. 2007, 1697-1704

[17] Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Beijing: Publishing House of Electronics Industry, 2004, 337-384

[18] Baeza-Yates R A, Ribeiro-Neto B. Modern Information Retrieval. Boston: Addison-Wesley, 1999

[19] Järvelin K, Kekäläinen J. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, 2002, 20(4): 422-446

[20] Kendall M G. A new measure of rank correlation. Biometrika, 1938, 30(1-2): 81-93

[21] Liu T Y, Xu J, Qin T, Xiong W, Li H. Letor: benchmark dataset for research on learning to rank for information retrieval. In: Proceedings of SIGIR 2007 Workshop on Learning to Rank for Information Retrieval. 2007, 3-10

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Higher Education Press and Springer-Verlag Berlin Heidelberg